HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline
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arXiv
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| Format: | Preprint |
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2021
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| author | Heidler, Konrad Mou, Lichao Baumhoer, Celia Dietz, Andreas Zhu, Xiao Xiang |
| author_facet | Heidler, Konrad Mou, Lichao Baumhoer, Celia Dietz, Andreas Zhu, Xiao Xiang |
| contents | Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the coastline. In contrast to this, a human annotator will usually keep a mental map of both segmentation and delineation when performing manual coastline detection. To take into account this task duality, we therefore devise a new model to unite these two approaches in a deep learning model. By taking inspiration from the main building blocks of a semantic segmentation framework (UNet) and an edge detection framework (HED), both tasks are combined in a natural way. Training is made efficient by employing deep supervision on side predictions at multiple resolutions. Finally, a hierarchical attention mechanism is introduced to adaptively merge these multiscale predictions into the final model output. The advantages of this approach over other traditional and deep learning-based methods for coastline detection are demonstrated on a dataset of Sentinel-1 imagery covering parts of the Antarctic coast, where coastline detection is notoriously difficult. An implementation of our method is available at \url{https://github.com/khdlr/HED-UNet}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2103_01849 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline Heidler, Konrad Mou, Lichao Baumhoer, Celia Dietz, Andreas Zhu, Xiao Xiang Computer Vision and Pattern Recognition Image and Video Processing Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the coastline. In contrast to this, a human annotator will usually keep a mental map of both segmentation and delineation when performing manual coastline detection. To take into account this task duality, we therefore devise a new model to unite these two approaches in a deep learning model. By taking inspiration from the main building blocks of a semantic segmentation framework (UNet) and an edge detection framework (HED), both tasks are combined in a natural way. Training is made efficient by employing deep supervision on side predictions at multiple resolutions. Finally, a hierarchical attention mechanism is introduced to adaptively merge these multiscale predictions into the final model output. The advantages of this approach over other traditional and deep learning-based methods for coastline detection are demonstrated on a dataset of Sentinel-1 imagery covering parts of the Antarctic coast, where coastline detection is notoriously difficult. An implementation of our method is available at \url{https://github.com/khdlr/HED-UNet}. |
| title | HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2103.01849 |